45 citations · 121 across the 12 of their papers we have counts for
12 papers
UniWorld: Autonomous Driving Pre-training via World Models
Chen Min, Dawei Zhao, Liang Xiao +2
In this paper, we draw inspiration from Alberto Elfes' pioneering work in 1989, where he introduced the concept of the occupancy grid as World Models for robots. We imbue the robot…
Contrastive Label Disambiguation for Self-Supervised Terrain Traversability Learning in Off-Road Environments
Hanzhang Xue, Xiaochang Hu, Rui Xie +4
Discriminating the traversability of terrains is a crucial task for autonomous driving in off-road environments. However, it is challenging due to the diverse, ambiguous, and platf…
Traversability Analysis for Autonomous Driving in Complex Environment: A LiDAR-based Terrain Modeling Approach
Hanzhang Xue, Hao Fu, Liang Xiao +3
For autonomous driving, traversability analysis is one of the most basic and essential tasks. In this paper, we propose a novel LiDAR-based terrain modeling approach, which could o…
UniScene: Multi-Camera Unified Pre-training via 3D Scene Reconstruction for Autonomous Driving
Chen Min, Liang Xiao, Dawei Zhao +2
Multi-camera 3D perception has emerged as a prominent research field in autonomous driving, offering a viable and cost-effective alternative to LiDAR-based solutions. The existing…
Adversarial and Random Transformations for Robust Domain Adaptation and Generalization
Liang Xiao, Jiaolong Xu, Dawei Zhao +3
Data augmentation has been widely used to improve generalization in training deep neural networks. Recent works show that using worst-case transformations or adversarial augmentati…
ORFD: A Dataset and Benchmark for Off-Road Freespace Detection
Chen Min, Weizhong Jiang, Dawei Zhao +4
Freespace detection is an essential component of autonomous driving technology and plays an important role in trajectory planning. In the last decade, deep learning-based free spac…
Occupancy-MAE: Self-supervised Pre-training Large-scale LiDAR Point Clouds with Masked Occupancy Autoencoders
Chen Min, Xinli Xu, Dawei Zhao +3
Current perception models in autonomous driving heavily rely on large-scale labelled 3D data, which is both costly and time-consuming to annotate. This work proposes a solution to…
Trajectory Prediction for Autonomous Driving with Topometric Map
Jiaolong Xu, Liang Xiao, Dawei Zhao +2
State-of-the-art autonomous driving systems rely on high definition (HD) maps for localization and navigation. However, building and maintaining HD maps is time-consuming and expen…
Attentional Graph Neural Network for Parking-slot Detection
Chen Min, Jiaolong Xu, Liang Xiao +3
Deep learning has recently demonstrated its promising performance for vision-based parking-slot detection. However, very few existing methods explicitly take into account learning…
Self-Supervised Domain Adaptation with Consistency Training
L. Xiao, J. Xu, D. Zhao +4
We consider the problem of unsupervised domain adaptation for image classification. To learn target-domain-aware features from the unlabeled data, we create a self-supervised prete…
Drosophila-Inspired 3D Moving Object Detection Based on Point Clouds
Li Wang, Dawei Zhao, Tao Wu +5
3D moving object detection is one of the most critical tasks in dynamic scene analysis. In this paper, we propose a novel Drosophila-inspired 3D moving object detection method usin…
Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning
Zhen He, Shaobing Gao, Liang Xiao +3
Long Short-Term Memory (LSTM) is a popular approach to boosting the ability of Recurrent Neural Networks to store longer term temporal information. The capacity of an LSTM network…